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On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing
March 19, 2024, 4:45 a.m. | Zeyan Liu, Zijun Yao, Fengjun Li, Bo Luo
cs.LG updates on arXiv.org arxiv.org
Abstract: With ChatGPT under the spotlight, utilizing large language models (LLMs) to assist academic writing has drawn a significant amount of debate in the community. In this paper, we aim to present a comprehensive study of the detectability of ChatGPT-generated content within the academic literature, particularly focusing on the abstracts of scientific papers, to offer holistic support for the future development of LLM applications and policies in academia. Specifically, we first present GPABench2, a benchmarking dataset …
abstract academic aim arxiv benchmarking chatgpt community cs.cl cs.cr cs.lg evaluation generated language language models large language large language models llms methodology paper spotlight study through type writing
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